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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (28 November 2023) | Viewed by 26072

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Special Issue Editors

Prof. Dr. Kairong Lin

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Guest Editor

Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: smart water; big data and the digital twin in water resources
Special Issues, Collections and Topics in MDPI journals

Dr. Jingwen Zhang

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Guest Editor

Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: water resources management; optimization; human–machine interaction; ecohydrology; agroecosystem models
Special Issues, Collections and Topics in MDPI journals

Dr. Pan Yang

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Guest Editor

Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
Interests: water resources systems analysis; food–energy–water nexus; big data in water resources

Special Issue Information

Dear Colleagues,

Recently, smart water systems and the digital twin have represented a hot topic among governments, industries, and researchers. Smart techniques (such as smart data collection, modelling, and real-time controlling) aim to improve traditional experience-based water systems via digital twin models in the virtual world. In this Special Issue, we invite submissions that address problems in smart water systems and the digital twin. Multi-source data from in situ sensors, crowdsourcing participants, unmanned aerial vehicles (UAVs), and satellites can provide different levels of information about water systems. To derive cost-effective and reliable water data, effective multi-source data fusion approaches that combine these datasets must be developed. Modelling papers are invited to improve the representation of all water pathways with mechanisms in water systems. With robust datasets and models, model–data fusion (calibration and data assimilation) is one of the most critical processes in building digital twin models. We also invite decision-making papers regarding smart water management based on AI-based machine learning or other real-time control and feedback approaches. This Special Issue provides an opportunity to facilitate the best available practices in smart water systems and to increase their adoption in water management practices.

Prof. Dr. Kairong Lin
Dr. Jingwen Zhang
Dr. Pan Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

multi-source data fusion
crowdsourcing data collection
process-based modelling
model–data fusion
smart water management
machine learning
real-time control and feedback
human-machine interaction
the digital twin in water resources

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Published Papers (9 papers)

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Research

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19 pages, 2128 KiB

Open AccessArticle

Reengineering and Its Reliability: An Analysis of Water Projects and Watershed Management under a Digital Twin Scheme in China

by Dong Sheng, Yu Lou, Feifei Sun, Jinping Xie and Yu Yu

Water 2023, 15(18), 3203; https://doi.org/10.3390/w15183203 - 8 Sep 2023

Cited by 7 | Viewed by 1656

Abstract

Water project and watershed management is currently being reengineered under digital twin schemes in China through pilot projects. An evaluation on pilot reengineering is important for its further implementation and improvement. This paper investigates national legislation and pilot projects’ implementations of a digital twin watershed and digital twin water project from the perspectives of design, policy, technology, investment, personnel, cyberspace security, co-construction, and sharing through interviews and expert review, and it uses a Bayesian network to study their reliability. First, the design of the digital twin watershed and digital twin water project is reasonable with regard to system architecture and business management. Second, although there are some national legislations on cyberspace security and geospatial data, they are incomplete for policy making and are probably infeasible for some technology. Third, there are insufficient mechanisms to sustainably support investment, personnel, and cyberspace security. Forth, co-construction and sharing are required for both inside and outside water departments. Fifth, the Bayesian network is useful for investigating the reliability of weak nodes, and it is helpful for the design and further implementation of the digital twin watershed and digital twin water project, as will be demonstrated with an anonymous example. This study could provide useful insights into the further reengineering of water projects and watershed management under a digital twin scheme in the world. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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16 pages, 478 KiB

Open AccessFeature PaperArticle

Digitalisation of the European Water Sector to Foster the Green and Digital Transitions

by Emanuele Quaranta, Helena M. Ramos and Ulf Stein

Water 2023, 15(15), 2785; https://doi.org/10.3390/w15152785 - 1 Aug 2023

Cited by 5 | Viewed by 3042

Abstract

During the Digital Decade, the European Union (EU) is facing two important challenges: the green (and energy) transition and the digital transition, which are interconnected with one another. These transitions are of high relevance in several aspects of our life, e.g., in the industry, energy sector, transports, environmental management and our daily life. Digital technologies are particularly emerging also as multi-benefit solution in the water sector, as water is becoming more and more vulnerable to climate change (e.g., droughts and floods) and human activities (e.g., pollution and depletion). Within this context, in this study we assessed some of the several economic benefits that digital solutions can bring to the water sector, with a focus on leakage reduction in water distribution networks, reduction of combined sewer overflows and improvement of hydropower generation and operation. The benefits are calculated for each EU Member State and the UK, and then aggregated at the EU scale. Benefits were quantified in EUR 5.0, 0.14 and 1.7 billion per year (EUR 13.2 per person per year, on average), respectively, excluding environmental and social benefits, which may play a non-negligible role. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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14 pages, 3276 KiB

Open AccessArticle

Historical Trends and Driving Forces of River Water Quality Improvement in the Megacity Shenzhen, China

by Xiang Sun, Qingping Wu, Jiping Jiang and Kairong Lin

Water 2023, 15(12), 2283; https://doi.org/10.3390/w15122283 - 18 Jun 2023

Viewed by 2215

Abstract

The water quality of urban rivers in China has undergone significant improvement since the 13th Five-Year Plan period (2016–2020). Among these, urban rivers in Shenzhen are the most representative. Assessing historical trends and analyzing the driving forces of river water quality improvement is of great importance and provides valuable insights. This study selects two typical watersheds, Maozhou River and Longgang River, to explore how water quality trends link with water control projects in Shenzhen from 2003 to 2020. The historical trends were evaluated using a recently developed index called WQI-DET, which considers DO, COD, NH₃-N, TP, and anionic surfactants. Results showed that both rivers were seriously polluted before 2010 and gradually improved during the 12th Five-Year Plan period. After 2010, the water quality improved rapidly thanks to the environmental remediation of the mainstream, especially the interception project of Longgang River around 2010, and the Maozhou River interception project in 2015. The rainwater and sewage diversion renovation project mainly contributed to meeting the standards for Class IV water bodies during the 13th Five-Year Plan period. This study reveals the semi-quantitative link between comprehensive water quality improvement and pollution control engineering measures. It is a helpful review for Shenzhen and provides a useful reference for other cities. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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16 pages, 6839 KiB

Open AccessArticle

Spatio-Temporal Characteristics and Trend Prediction of Extreme Precipitation—Taking the Dongjiang River Basin as an Example

by Ningning Li, Xiaohong Chen, Jing Qiu, Wenhui Li and Bikui Zhao

Water 2023, 15(12), 2171; https://doi.org/10.3390/w15122171 - 8 Jun 2023

Cited by 3 | Viewed by 1933

Abstract

The intricate interplay between human activities and climate change has resulted in a rise in the occurrence of extreme precipitation worldwide, which has attracted extensive attention. However, there has been limited dissemination of accurate prediction of extreme precipitation based on analysis of spatio-temporal characteristics of such events. In this study, the intra-annual distribution of extreme precipitation was subjected to scrutiny via an analysis of precipitation concentration degree (PCD) and precipitation concentration period (PCP), while also investigating the spatio-temporal trends of the annual precipitation, maximum daily precipitation, maximum 5-day precipitation, and extreme precipitation (defined as daily precipitation exceeding the 99th percentile of the total precipitation). Furthermore, subsequently, conducting simulation, verification, and prediction of extreme precipitation was achieved through the application of a back-propagation artificial neural network (BP-ANN). This study employed the data of the daily precipitation in the Dongjiang River Basin from 1979 to 2022, a time period which was of sufficient length to reflect the latest changes in precipitation patterns. The results demonstrated spatio-temporal differences between precipitation levels in the upper and lower reaches of the Dongjiang River Basin, that is, the PCD of the lower reach was higher and the PCP of the lower reach came half a month later compared with the upper reach. Moreover, the extreme precipitation indices increased from northeast to southwest, with the characteristics of lower-reach precipitation being more extreme and periodic. It was predicted that the total precipitation in 2023 would decrease, while the extreme precipitation would increase. The qualification rate of forecasting extreme precipitation ranged from 27% to 72%. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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19 pages, 11080 KiB

Open AccessArticle

A Digital Twin Dam and Watershed Management Platform

by DongSoon Park and Hojun You

Water 2023, 15(11), 2106; https://doi.org/10.3390/w15112106 - 1 Jun 2023

Cited by 13 | Viewed by 5827

Abstract

This paper presents an innovative digital twin dam and watershed management platform, K-Twin SJ, that utilizes real-time data and simulation models to support decision-making for flood response and water resource management. The platform includes a GIS-based geospatial digital twin of the entire Sumjin dam and river water system in Korea, with high-precision geospatial topography and facility information for dams and rivers (watershed area 4913 km², river length 173 km, and 91 water infrastructures). The platform synchronizes real-time data such as rainfall, dam and river water levels, flow rate, and closed-circuit television (CCTV), and incorporates three hydraulic and hydrological simulation models for efficient dam operation considering the river conditions. AI technology is also used to predict the river water level and suggest optimal dam discharge scenarios. Additionally, the platform includes a geotechnical safety evaluation module for river levees, advanced drone monitoring for dams and rivers, and an AI CCTV video surveillance function. The digital-twin-based platform supports efficient decision-making for smart flood responses and contributes to reducing flooding damage and optimal operation through better smart water management. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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11 pages, 4621 KiB

Open AccessArticle

Optimizing the Pump Storage System for Hot Water Showering at Swimming Pools

by Ling-Tim Wong, Chun-San Chan, Kwok-Wai Mui and Dadi Zhang

Water 2023, 15(11), 2083; https://doi.org/10.3390/w15112083 - 31 May 2023

Viewed by 1819

Abstract

Previous studies have demonstrated the energy- and water-saving potentials of showering facilities in residential buildings. However, the prospect of public showering places where multiple showerheads usually worked together according to their opening hours has often been overlooked and rarely investigated. This study measured the water flow rate in a water supply pipe to understand the water-use patterns and water consumption of showering facilities in a swimming pool. The measurements were carried out on typical cold and warm days. The results showed that the average water consumption was 50.5 L/person in December (T = 19.7 °C) and 38.6 L/person in April (T = 24.5 °C). The fluctuation of the water flow rate demonstrated a water demand pattern for the showering facilities, where the maximum water flow rate was more than twice the average level, indicating inefficient working modes of the water supply pump. To improve the current situation, an appropriately sized water tank was suggested to be installed, which could ensure a more stable water flow rate in the main supply pipe, enhancing the water supply system efficiency and saving energy for the water pump. These results contribute to establishing the design data for optimizing water tank design in swimming pools or similar buildings with public showering demand and illustrate the energy-saving potential of water supply systems in showering facilities. Nevertheless, the results of this study are only based on theoretical calculations. More comprehensive field studies with a water tank are required to confirm these findings and better elucidate the effects. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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20 pages, 5968 KiB

Open AccessArticle

Machine Learning Framework with Feature Importance Interpretation for Discharge Estimation: A Case Study in Huitanggou Sluice Hydrological Station, China

by Sheng He, Geng Niu, Xuefeng Sang, Xiaozhong Sun, Junxian Yin and Heting Chen

Water 2023, 15(10), 1923; https://doi.org/10.3390/w15101923 - 19 May 2023

Cited by 2 | Viewed by 1937

Abstract

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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19 pages, 6896 KiB

Open AccessArticle

Research on Optimal Allocation of Water Resources in Handan City Based on the Refined Water Resource Allocation Model

by Jing Ma, Hongliang Liu, Wenfeng Wu, Yinqin Zhang and Sen Dong

Water 2023, 15(1), 154; https://doi.org/10.3390/w15010154 - 30 Dec 2022

Cited by 10 | Viewed by 2333

Abstract

In order to realize the dynamic regulation and control of regional water resources and alleviate the imbalance between supply and demand of regional water resources, on the basis of the demand of refined management of water resources, the dynamic General Water Allocation and Simulation Model (GWAS) of Handan City was constructed. The research on the optimal allocation of water resources in different regions and counties under different normal and dry years in the planning years (2025 and 2035) was carried out. The results show that the allocated water volume in the normal and dry years of Handan in 2025 is 2.248 billion m³ and 2.150 billion m³, respectively, and the water shortage rate is 11.72% and 22.11%, respectively. The water shortage is mainly in agriculture. In 2035, the allocated water volumes in normal and dry years will be 2.504 billion m³ and 2.33 billion m³, respectively, and the water shortage rates will be 4.50% and 21.84%, respectively. After optimized allocation, the water supply structure was significantly improved. The proportion of groundwater supply will decrease at each planning level year, and the water supply of external water transfer and unconventional water will increase. This research can provide technical reference to the Handan development scheme depending on water resources in the future, as well as the optimal allocation of water resources in other cities in the Beijing–Tianjin–Hebei Region. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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Review

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31 pages, 2099 KiB

Open AccessReview

Remote Sensing Technology in the Construction of Digital Twin Basins: Applications and Prospects

by Xiaotao Wu, Guihua Lu and Zhiyong Wu

Water 2023, 15(11), 2040; https://doi.org/10.3390/w15112040 - 27 May 2023

Cited by 10 | Viewed by 3857

Abstract

A digital twin basin serves as a virtual representation of a physical basin, enabling synchronous simulation, virtual–real interaction, and iterative optimization. The construction of a digital twin basin requires a basin database characterized by large-scale coverage, high-precision, high-resolution, and low-latency attributes. The advancements in remote sensing technology present a new technical means for acquiring essential variables of the basin. The purpose of this paper was to provide a comprehensive overview and discussion of the retrieval principle, data status, evaluation and inter-comparison, advantages and challenges, applications, and prospects of remote sensing technology in capturing seven essential variables, i.e., precipitation, surface temperature, evapotranspiration, water level, river discharge, soil moisture, and vegetation. It is indicated that remote sensing can be applied in some digital twin basin functions, such as drought monitoring, precipitation forecasting, and water resources management. However, more effort should be paid to improve the data accuracy, spatiotemporal resolution, and latency through data merging, data assimilation, bias correction, machine learning algorithms, and multi-sensor joint retrieval. This paper will assist in advancing the application of remote sensing technology in constructing a digital twin basin. Full article

(This article belongs to the Special Issue Smart Water and the Digital Twin)

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